Representative elementary area issue in soil spectral measurements

Kaźmierowski, Cezary; Herodowicz, Karolina; Królewicz, Sławomir; Nowosad, Jakub; Piekarczyk, J.

doi:10.1016/j.still.2018.11.002

Cited by 5 publications

(7 citation statements)

References 57 publications

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“…The effects of soil surface roughness on reflectance spectra play a key role in the interpretation of remote sensing data (Herodowicz, 2017; Kaźmierowski et al., 2019; Piekarczyk et al., 2016; Richter et al., 2009). Piekarczyk et al.…”

Section: Resultsmentioning

confidence: 99%

“…The effects of soil surface roughness on reflectance spectra play a key role in the interpretation of remote sensing data (Herodowicz, 2017;Kaźmierowski et al, 2019;Piekarczyk et al, 2016;Richter et al, 2009). Piekarczyk et al (2016) defined three classes of soil surface roughness, prepared under laboratory conditions, according to HSD and T 3D indices.…”

Section: Categories Of Soil Surface Roughnessmentioning

confidence: 99%

“…Predictive soil surface roughness models can be applied effectively for a range of tasks, such as estimating water retention in soil and the intensity and speed of surface water flow. reflectance spectra in remote sensing (Croft et al, 2012;Herodowicz, 2017;Kaźmierowski et al, 2019) and can complicate the prediction of soil properties (Piekarczyk et al, 2016), which is especially important in precision agriculture (Nanni & Demattê, 2006). Finally, as the roughness of the ground influences the amount of shortwave solar radiation reflected back into the atmosphere, it also can influence the climate (Cierniewski et al, 2015).…”

Section: Plain Language Summarymentioning

confidence: 99%

“…Information about soil roughness can also be used as an input parameter for models predicting erosion intensity (Gilliot et al., 2017) and in determining the microbiological activity (Or et al., 2007) and biological quality of soils (Young et al., 2001). In addition, soil surface roughness strongly modifies the reflectance spectra in remote sensing (Croft et al., 2012; Herodowicz, 2017; Kaźmierowski et al., 2019) and can complicate the prediction of soil properties (Piekarczyk et al., 2016), which is especially important in precision agriculture (Nanni & Demattê, 2006). Finally, as the roughness of the ground influences the amount of shortwave solar radiation reflected back into the atmosphere, it also can influence the climate (Cierniewski et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters

Herodowicz‐Mleczak

Piekarczyk

Kaźmierowski

et al. 2022

J Adv Model Earth Syst

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The quantitative description of the soil surface roughness is necessary for effective monitoring of wind, water and tillage erosion, hydrological processes or greenhouse gas emissions. The aim of this work was to build soil roughness predictive models based on the type of tillage tool, the roughness indices and soil properties. The roughness formed by five tillage tools was determined. Two surface roughness indices: Height Standard Deviation (HSD) and T3D (Tortuosity index) were calculated from Digital Elevation Model. The both roughness indices demonstrated a significant correlation, however, they provided different information about soil roughness. The HSD describes roughness on the “macro,” while T3D refers to the “micro” scale. Hence, our findings show that a single index is not sufficient to describe the roughness of post‐treatment surface. The linear and random forest models were built to describe the relationships between the roughness indices, type of tillage tool and soil properties. The HSD analysis indicated that the type of tillage tools had the greatest impact on post‐treatment roughness. In contrast, T3D analysis found soil texture to have a significant effect, together with tillage tools. In all modeling scenarios, T3D was more accurately predicted than HSD by both the linear (R2 = 0.62 vs. R2 = 0.60) and random forest models (R2 = 0.58 vs. R2 = 0.55). Predictive soil surface roughness models can be applied effectively for estimating water retention in soil, the intensity and speed of surface water flow, soil erosion, the level of reflected shortwave solar radiation or soil properties by remote sensing techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Categories Of Soil Surface Roughnessmentioning

confidence: 99%

Section: Plain Language Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters

Herodowicz‐Mleczak

Piekarczyk

Kaźmierowski

et al. 2022

J Adv Model Earth Syst

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“…Despite the differences, certain regularities were observed between the spectra of soils with different roughness states and after successive simulated rainfalls (Figure 7). The initial level of roughness appears to have a significant influence on the initial level of the spectra, insofar that a higher roughness state was correlated with a lower spectral value (Cierniewski et al., 2002; Kaźmierowski et al., 2019; Richter et al., 2005; Wu et al., 2009). In addition, the initially roughest soils (R3) demonstrated the greatest differences in spectra level between successive rainfall simulations.…”

Section: Resultsmentioning

confidence: 99%

Estimation of Soil Surface Roughness Parameters Under Simulated Rainfall Using Spectral Reflectance in Optical Domain

Herodowicz‐Mleczak,

Piekarczyk,

Ratajkiewicz

et al. 2023

Earth and Space Science

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The purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS‐NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T3D (Tortuosity index). Although both shared a significant correlation with SSR, the T3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T3D was obtained with the raw spectra (RAW) (R2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R2 = 0.71) or the Savitzky‐Golay (SG) method (R2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs ‐ Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725–820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450–772 nm) were most important in models based on transformed spectra.

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Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization

Deng

Lü

et al. 2020

Geoderma

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Representative elementary area issue in soil spectral measurements

Cited by 5 publications

References 57 publications

Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters

Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters

Estimation of Soil Surface Roughness Parameters Under Simulated Rainfall Using Spectral Reflectance in Optical Domain

Evaluating the characteristics of soil vis-NIR spectra after the removal of moisture effect using external parameter orthogonalization

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